With increases in the use of computers to collect and store data and with increases in computer based transactions, such as over the Internet, there has been a proliferation of databases containing large amounts of historical data commonly referred to as “data warehouses.” For example, as more and more data is collected regarding consumer purchase and/or shopping habits, this data may be stored in a data warehouse for subsequent analysis. Other uses of data warehouses include, for example, data warehouses of genetic or other scientific data.
While the particular data may vary for different data warehouses, in general, data warehouses are databases of historical data that may utilize a “star-schema” database structure. A data warehouse is typically present to users through a multi-dimensional hypercube and provides an ad hoc query environment. Furthermore, the data warehouse will, typically, contain a large amount of data and have a complex structure.
The multi-dimensional hypercube, typically includes several “dimensions” where each dimension includes “members.” The members of a dimension may have a hierarchical structure. A “measure” of a dimension or dimensions may be incorporated into a data warehouse as a pre-calculated value. Thus, a measure is a computer member. For example, a measure may be incorporated into a meta-outline of a data warehouse. In such a way, the pre-calculated “measure” may be made available to users of the data warehouse. Pre-calculated measures of dimensions of a data warehouse are sometimes referred to as “analytics” of a data warehouse.
Because of the size and complexity of data warehouses, they are typically created, administered and maintained by an information technology specialist. As such, creation, modification and/or analysis of data warehouses may be a costly and time consuming proposition.
For example, in creating a data warehouse, an enterprise data architecture is typically analyzed and represented in the data warehouse. After this analysis, the data is extracted, transformed and loaded into the data warehouse from other, dissimilar databases. This analysis and creation of the data warehouse architecture and the extraction, transformation and loading of data may be very costly and time consuming. As such, the usefulness and/or timeliness of data warehouse applications may be reduced.
Furthermore, the data warehouse star-schema database and integration hub used for integrating data in the data warehouse are, conventionally, separate isolated applications even though the data warehouse contains the superset of data which includes the transaction information in the hub. The information in the integration hub is not transparent to the warehouse. The integration hub transforms the data once for integration purposes and is, typically, managed and/or created by information technology experts that understand the data format, type and meaning and are relied on to transform, extract, and load the data again.
Recently, Enterprise Application Integration (EAI) and/or Business Process Integration (BPI) have been utilized to integrate multiple applications through enterprise application techniques, such as integration brokers and/or integration buses. Furthermore, these tools have been extended to manage business processes through business process integration techniques. These application and/or business process integration techniques, collectively and individually, are referred to herein as an integration node. The integration node provides business objects that characterize business information and/or transactions. These business objects, therefore, reflect the business processes of a business and/or the data about such business processes.